There is a technology of Condition Based Maintenance (CBM) in which, by mounting a plurality of sensors (measurement devices) for measuring a state on an energy conversion apparatus (facility) or the like which converts fuel to at least kinetic energy, thermal energy or electric energy typified by a combined heat and power (cogeneration) apparatus, each state of the apparatus is measured and grasped at every moment to judge normality or abnormality of the state of the apparatus based on the data from the sensors (referred to as apparatus state measurement data, sensor data or the like), thereby performing maintenance in consideration of the abnormal state. This is effective in reducing maintenance cost.
Japanese Unexamined Patent Application Publication No. 2002-110493 (Patent Document 1) and Japanese Unexamined Patent Application Publication No. 2000-252180 (Patent Document 2) describe a method of a multistage multivariate analysis for quality variation cause analysis on manufacturing line, in which a plurality of explanatory variables are divided into groups including a predetermined small number of variables, linear multiple regression model creation (Yi=A·Xi) is applied to all divided groups to narrow down the explanatory variables in each divided group with a forward selection method, the narrowed-down explanatory variables are combined and multiple regression model creation is applied again, and these steps are repeated in multiple stages.
U.S. Pat. No. 7,209,846 (U.S. Pat. No. 7,209,846 B2) (Patent Document 3) describes a method of performing a causal analysis between product quality on a manufacturing line and process data by using a graphical model.
Non-Patent Document 1 describes statistical models. Specifically, a GLM (Generalized Linear Model) method, a GAM (Generalized Additive Model) method, and a nonlinear model method are described.
Non-Patent Document 2 describes a plurality of methods for creating a degenerate linear regression model (Y=A·X) of an objective variable (Y) and an explanatory variable (X) based on a Projection Method for avoiding a non-computable problem or insufficient accuracy due to a Multiple Co-linear phenomenon caused by simultaneous fluctuations of a plurality of elements of the explanatory variables. Specifically, a PLS (Partial Least Squares) method, a PCR (Principal Component Regression) method, a Ridge method, and a Lasso method are described. Also, as a nonlinear relation model creating method, a nonlinear regression method is described. Specifically, a GLM (Generalized Linear Model) method and a MARS (Multivariate Adaptive Regression Splines) method are described. Also, as a sampling method for finding a model coefficient in combination with a Bayes method, a MCMC (Markov Chain Monte Carlo) method is described.
Non-Patent Document 3 describes a method of constructing a linear regression prediction model by mixing data items having collinearity by using a PLS (Partial Least Squares) method.
Non-Patent Document 4 describes a method of a statistical mathematical general algorithm performing a causal analysis by using a graphical model.